euclidean_distance_from_a_to_b#

rojak.atmosphere.regions.euclidean_distance_from_a_to_b(from_feature, to_feature, sampling=1)[source]#

Euclidean distance from feature A to feature B

For example, to find the distance of a contrail forming region to a turbulent region, then from_region would be a boolean array of where the contrail region is and to_region would be a boolean array of where turbulence is present.

Note: Description of sampling arg has been copied from scipy.ndimage.distance_transform_edt()

Parameters:

  • from_feature (ndarray) – Feature to compute distances from

  • to_feature (ndarray) – Feature to compute distances to

  • sampling (float | Sequence[float]) – Spacing of elements along each dimension. If a sequence, must be of length equal to the input rank;

  • number (if a single)

  • specified (this is used for all axes. If not)

  • implied. (a grid spacing of unity is)

Returns:

Array of the closest distance from the point each feature A to feature B. Any points where feature A is not present will have the value np.nan

Return type:

ndarray

Examples

Modified from the docstring of scipy.ndimage.distance_transform_edt()

>>> to_b = np.array(([0,1,1,1,1],
...                  [0,0,1,1,1],
...                  [0,1,1,1,1],
...                  [0,1,1,1,0],
...                  [0,1,1,0,0]), dtype=bool)
>>> to_b = ~to_b
>>> from_a = np.ones_like(to_b)
>>> euclidean_distance_from_a_to_b(from_a, to_b)
array([[0.        , 1.        , 1.41421356, 2.23606798, 3.        ],
       [0.        , 0.        , 1.        , 2.        , 2.        ],
       [0.        , 1.        , 1.41421356, 1.41421356, 1.        ],
       [0.        , 1.        , 1.41421356, 1.        , 0.        ],
       [0.        , 1.        , 1.        , 0.        , 0.        ]])

If the feature we are computing distances from is never present, then all values in the array will be np.nan

>>> np.isnan(euclidean_distance_from_a_to_b(np.zeros_like(to_b), to_b)).all()
np.True_

With a sampling of 2 units along x, 1 along y:

>>> euclidean_distance_from_a_to_b(from_a, to_b, sampling=[2, 1])
array([[0.        , 1.        , 2.        , 2.82842712, 3.60555128],
       [0.        , 0.        , 1.        , 2.        , 3.        ],
       [0.        , 1.        , 2.        , 2.23606798, 2.        ],
       [0.        , 1.        , 2.        , 1.        , 0.        ],
       [0.        , 1.        , 1.        , 0.        , 0.        ]])